Listen, Look, and Find the One

Author:

Wang Xiao1ORCID,Liu Wu2,Chen Jun3,Wang Xiaobo2,Yan Chenggang4,Mei Tao2

Affiliation:

1. NERCMS, School of Computer Sicence, Wuhan University

2. AI Research of JD.com

3. NERCMS, School of Computer Science, Wuhan University

4. Hangzhou Dianzi University

Abstract

Person search with one portrait, which attempts to search the targets in arbitrary scenes using one portrait image at a time, is an essential yet unexplored problem in the multimedia field. Existing approaches, which predominantly depend on the visual information of persons, cannot solve problems when there are variations in the person’s appearance caused by complex environments and changes in pose, makeup, and clothing. In contrast to existing methods, in this article, we propose an associative multimodality index for person search with face, body, and voice information. In the offline stage, an associative network is proposed to learn the relationships among face, body, and voice information. It can adaptively estimate the weights of each embedding to construct an appropriate representation. The multimodality index can be built by using these representations, which exploit the face and voice as long-term keys and the body appearance as a short-term connection. In the online stage, through the multimodality association in the index, we can retrieve all targets depending only on the facial features of the query portrait. Furthermore, to evaluate our multimodality search framework and facilitate related research, we construct the Cast Search in Movies with Voice (CSM-V) dataset, a large-scale benchmark that contains 127K annotated voices corresponding to tracklets from 192 movies. According to extensive experiments on the CSM-V dataset, the proposed multimodality person search framework outperforms the state-of-the-art methods.

Funder

Fundamental Research Funds for the Central Universities

National Nature Science Foundation of China

National Key R8D Program of China

Hubei Province Technological Innovation Major Project

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture

Cited by 16 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A novel deep learning technique for medical image analysis using improved optimizer;Health Informatics Journal;2024-04

2. Generative adversarial network-based algorithm for 3D construction of pedestrians;2023 IEEE 4th International Conference on Pattern Recognition and Machine Learning (PRML);2023-08-04

3. Segmentation quality assessment network-based object detection and optimized CNN with transfer learning for yoga pose classification for health care;Soft Computing;2023-07-27

4. Sign Language Detection and Recognition using CNN;2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS);2023-06-14

5. Sequential Transfer Learning Models with Additional Layers for Pneumonia Diagnosis;2023 International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3);2023-06-08

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3